Kernel Launch & Occupancy Basics: Pitfalls & Red Flags

Pitfalls and red flags

Pitfalls & Red Flags for Kernel Launch & Occupancy Basics centers on theoretical vs achieved occupancy, latency hiding score, and warp starvation rate. The objective is to connect profiler evidence to root-cause mechanism and release-safe action.

• Optimizing occupancy while ignoring memory transaction inflation.

• Comparing profiler captures across mismatched toolchain revisions.

• Treating average throughput as sufficient without p95/p99 tail checks.

• Skipping mixed-workload validation for graphics-plus-compute products.

• Closing issues without explicit owner and reproducible regression evidence.

Ownership check

GPU OWNERSHIP LAYERS — Kernel Launch & Occupancy Basics

artifact area     owner
----------------  ----------------------------
architecture    kernel optimization owner
RTL/microarch   compiler owner
software/tools  GPU performance lead

Rule: each metric needs a named owner before signoff.

GPU deep dive

SIMT abstractions are productive only when launch geometry and divergence behavior align with hardware.

Concept diagram

PROGRAMMING MODEL STACK

host API -> kernel launch -> grid -> block -> warp -> lane

Metric graph

KERNEL EFFICIENCY TREND

warp execution efficiency  ██████████
memory replay ratio        █████
idle issue slots           ███

Reports and artifacts

• occupancy report

• warp efficiency summary

• kernel launch audit

• replay counter snapshot

Mini case study

A block-size bump improved theoretical occupancy but increased replay and reduced achieved throughput by 22%.

Debug branches

• Map launch geometry to active warps per SM

• Correlate branch masks with divergence hotspots

• Validate occupancy against achieved IPC

Senior review question

Ask: which metric and benchmark pairing proves this topic is truly closed in production context?

Key takeaways

• Always pair micro-kernel metrics with end-to-end workload impact.

• Lock toolchain, driver, and launch metadata before comparing performance results.

Common pitfalls

• Optimizing occupancy without checking memory-system saturation.

• Comparing profiler captures from different driver or compiler builds.

• Declaring wins without reproducible accuracy and performance gates.

Why common mistakes happen

GPU teams fall into metric traps because GPUs expose many counters that look authoritative. Occupancy, utilization, bandwidth, and hit rate are each useful, but each can mislead when read without context.

Another trap is benchmark overfitting. A fix can improve a microbenchmark by aligning perfectly with its shape while harming scenes, kernels, or deployment conditions that matter more to the product.

The senior review habit is to ask what would disprove the current explanation. If no one can name a counter, trace, or workload that could falsify the hypothesis, the explanation is not yet strong enough.